Bulk RNAseq report - Differential Expression Analysis based on Aneuploidy levels
Annamaria Piva
25 June, 2025
Last updated: 2025-06-25
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Knit directory: casper_ss_ma/analysis/
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Introduction
The goal of this analysis is to identify which pathways are up- or down-regulated in samples with high or low levels of Aneuploidy Score (AS) computed using CaSPeR pipeline. For each condition (Healthy, HR+, and TNBC), patients are divided into high and low aneuploidy score groups. The following comparisons:
HR+ High-AS vs HR+ Low-AS
TNBC High-AS vs TNBC Low-AS
Healthy High-AS vs Healthy Low-AS
Overview of the analysis step
The analysis includes:
- Differential gene expression analysis with DESeq2
- Gene set enrichment analysis with ClusterProfiler
- Gene set enrichment analysis with fastGSEA with the curated Human MSigDB Collections. In particular the hallmark gene sets summarize and represent specific well-defined biological states or processes.
- Focus on NF-kB pathway. The R package pathview allows to visualize differentially expressed genes in the KEGG pathway NF-kB pathway
The input for the following analysis is:
- counts matrix, produced by Salmon and normalized with variance stabilizing transformation (VST) normalization using Deseq2, where each row represents one sample and each column represents one gene, so each cell represents the expression level of a specific gene in a particular sample. VST aims at generating a matrix of values for which variance is constant across the range of mean values, especially for low mean;
- samples info, including sample name, condition (HRplus, TNBC, Healthy) and batch (240919_rnaseq, 250501_rnaseq).
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)Loading R packages and input data
The first steps to start the analysis in R is to load the packages required for the analysis, load the input data mentioned above and establish the thresholds for the analysis:
- min_sample = 2, minimum number of samples where the gene needs to have at least 1 read;
- logfc = log2(2) = 1, which represents the ratio between the expression level of a gene in the conditions considered, expressed in logarithmic scale (base 2); a positive log fold change for a gene, greater than 1, by a multiplicative factor 2^logfc;
- qvalue = 0.01, that can be interpreted as false positive rate, the proportion of false positives among all positive results, which means avoid to detect differential expression of a gene that is not differentially expressed. LogFC and qvalue thresholds have been selected based on commonly used thresholds.
Stratification of samples by Aneuploidy score
To classify samples into High and Low Aneuploidy Score groups, we examined the distribution of aneuploidy scores across all samples from the three conditions. The distribution appeared bimodal, suggesting the presence of two distinct populations. To separate these, we defined a cutoff at the local minimum between the two peaks.
In the distribution plots:
The blue line indicates the median aneuploidy score of the displayed samples.
The red line marks the cutoff point, corresponding to the local minimum used to define the High vs Low groups.
| Version | Author | Date |
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| a039b3f | annamariapiva | 2025-06-20 |
| Version | Author | Date |
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| a039b3f | annamariapiva | 2025-06-20 |
| Version | Author | Date |
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| a039b3f | annamariapiva | 2025-06-20 |
Differential expression analysis
Differential expression analysis is performed using a custom function, which accounts for batch effect. A batch effect occurs when non-biological factors, like laboratory conditions or instruments used, in an experiment cause changes in the data produced by the experiment. Lowly expressed genes are removed to reduce noise. Lowly expressed genes are here considered as:
- genes having total number of reads less than half of the samples, 11;
- genes expressed in less samples than the number of conditions, 2.
PCA
Let’s have a look at PCA, and gene expression pattern across samples. The batch effect has been considered in the design, but has not been corrected for this plot.
Contrast 1: HRplus High-AS vs Low-AS
PCA of selected conditions
Here is the PCA of selected sample from the first comparison.
MA plot and volcano plot
Genes are annotated as significant or not, to distinguish between genes showing meaningful changes, that is having an adjusted p-value below the threshold considered above and an absolute log2FoldChange greater than the cutoff considered above.
Table of all differentially expressed genes
Heatmap for top 20 genes
Given the significant genes, among the differentially expressed genes previously computed, let’s visualize the top20 and all the DE genes.
Meaning of Colors
- Red: Indicates high expression for that gene in a given sample (value above average, positive compared to the standardized scale).
- Blue: Indicates low expression for that gene in a given sample (value below average, negative compared to the standardized scale).
- White (or intermediate color): Indicates an expression close to the average (standardized value around 0).
Gene set enrichment analysis
Further analysis is done through gene set enrichment analysis, which does not exclude genes based on logfc or adjusted p-value, as done previously. GSEA is performed separately on each subontology: biological processes (BP), cellular components (CC) and molecular functions (MF). The dot plot below shows the top 10 most enriched GO terms. The size of each dot correlates with the count of differentially expressed genes associated with each GO term. Furthermore, the color of each dot reflects the significance of the enrichment of the respective GO term, highlighting its relative importance.
Biological Processes (BP)
Molecular function (MF)
Pathway viewer: focus on NF-kB signaling pathway (KEGG id: hsa04064)
To visualize gene expression changes on biological pathways, we used the pathview R package, which maps gene-level statistics (e.g., log2 fold-changes) onto KEGG pathway diagrams.
For each contrast in our differential expression analysis, we extracted significantly differentially expressed genes and passed their log2 fold-change values to pathview() to visualize the NF-kappa B signaling pathway (KEGG pathway ID “hsa04064”). Pathway visualizations highlight upregulated and downregulated genes in red and blue, respectively, based on log2 fold-change.
[1] "Note: 4590 of 14347 unique input IDs unmapped."[1] "Note: 4590 of 14347 unique input IDs unmapped."[1] "Note: 4590 of 14347 unique input IDs unmapped."Contrast 2: TNBC High-AS vs Low-AS
PCA of selected conditions
Here is the PCA of selected sample from the second comparison.
MA plot and volcano plot
Genes are annotated as significant or not, to distinguish between genes showing meaningful changes, that is having an adjusted p-value below the threshold considered above and an absolute log2FoldChange greater than the cutoff considered above.
Heatmap for top 20 genes
Given the significant genes, among the differentially expressed genes previously computed, let’s visualize the top20 and all the DE genes.
Meaning of Colors
- Red: Indicates high expression for that gene in a given sample (value above average, positive compared to the standardized scale).
- Blue: Indicates low expression for that gene in a given sample (value below average, negative compared to the standardized scale).
- White (or intermediate color): Indicates an expression close to the average (standardized value around 0).
Gene set enrichment analysis
Biological Processes (BP)
Molecular function (MF)
Contrast 3: Healthy High-AS vs Low-AS
PCA of selected conditions
Here is the PCA of selected sample from the third comparison.
MA plot and volcano plot
Genes are annotated as significant or not, to distinguish between genes showing meaningful changes, that is having an adjusted p-value below the threshold considered above and an absolute log2FoldChange greater than the cutoff considered above.
Heatmap for top 20 genes
Given the significant genes, among the differentially expressed genes previously computed, let’s visualize the top20 and all the DE genes.
Meaning of Colors
- Red: Indicates high expression for that gene in a given sample (value above average, positive compared to the standardized scale).
- Blue: Indicates low expression for that gene in a given sample (value below average, negative compared to the standardized scale).
- White (or intermediate color): Indicates an expression close to the average (standardized value around 0).
Gene set enrichment analysis
Common pathways
Molecular Functions Pathways
GSEA Heatmap Hallmarks - all comparisons
HRplus-HighAS_vs_HRplus-LowAS
HALLMARK_ADIPOGENESIS 0.3195648
HALLMARK_ALLOGRAFT_REJECTION NA
HALLMARK_ANDROGEN_RESPONSE -0.7965541
HALLMARK_ANGIOGENESIS -1.2871963
HALLMARK_APICAL_JUNCTION NA
HALLMARK_APICAL_SURFACE 0.5208629
TNBC-HighAS_vs_TNBC-LowAS HLT-HighAS_vs_HLT-LowAS
HALLMARK_ADIPOGENESIS 0.8142424 -1.2551732
HALLMARK_ALLOGRAFT_REJECTION 0.9522570 -2.0372233
HALLMARK_ANDROGEN_RESPONSE 0.5914140 -0.9797684
HALLMARK_ANGIOGENESIS 0.8446955 -1.2955316
HALLMARK_APICAL_JUNCTION 1.1562512 -1.0381009
HALLMARK_APICAL_SURFACE 0.8373958 0.7755034
| Version | Author | Date |
|---|---|---|
| a039b3f | annamariapiva | 2025-06-20 |
Table of all genes
R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.4.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
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locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Rome
tzcode source: internal
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
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[27] DT_0.33
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